Tutorial

This tutorial covers downloading NEON Aquatic Instrument Subsystem (AIS) and Aquatic Observation Subsystem (AOS) data products using the neonUtilities R package, as well as basic instruction in beginning to explore and work with the downloaded data. This includes navigating data packages documentation, summarizing data for plotting and analysis, combining data within and between data products, and visualizing AIS and AOS data separately and together.

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Objectives

After completing this activity, you will be able to:

Things You’ll Need To Complete This Tutorial

To complete this tutorial you will need R (version >3.4) and, preferably, RStudio loaded on your computer.

Install R Packages

  • neonUtilities: Basic functions for accessing NEON data
  • neonOS: Basic data wrangling for NEON Observational Data
  • tidyverse: Collection of R packages designed for data science
  • plotly: Tool for creating interactive, web-based visualizations
  • vegan: Functions for analyzing ecological data

These packages are on CRAN and can be installed by install.packages().

Download Files and Load Directly to R: loadByProduct()

The most popular function in neonUtilities is loadByProduct(). This function downloads data from the NEON API, merges the site-by-month files, and loads the resulting data tables into the R environment, assigning each data type to the appropriate R class. This is a popular choice because it ensures you’re always working with the most up-to-date data, and it ends with ready-to-use tables in R. However, if you use it in a workflow you run repeatedly, keep in mind it will re-download the data every time.

Before we get the NEON data, we need to install (if not already done) and load the neonUtilities R package, as well as other packages we will use in the analysis.

# # Install neonUtilities package if you have not yet.
# install.packages("neonUtilities")
# install.packages("neonOS")
# install.packages("tidyverse")
# install.packages("plotly")
# install.packages("vegan")
# Set global option to NOT convert all character variables to factors
options(stringsAsFactors=F)

# Load required packages
library(neonUtilities)
library(neonOS)
library(tidyverse)
library(plotly)
library(vegan)

The inputs to loadByProduct() control which data to download and how to manage the processing. The following are frequently used inputs:

There are additional inputs you can learn about in the Use the neonUtilities R Package to Access NEON Data tutorial.

The dpID is the data product identifier of the data you want to download. The DPID can be found on the Explore Data Products page.

It will be in the form DP#.#####.###. For this tutorial, we’ll use some data products collected in NEON’s aquatics program:

Now it’s time to consider the NEON field site of interest. If not specified, the default will download a data product from all sites. The following are 4-letter site codes for NEON’s 34 aquatics sites as of 2025:

ARIK = Arikaree River CO
BARC = Barco Lake FL
BIGC = Upper Big Creek CA
BLDE = Black Deer Creek WY
BLUE = Blue River OK
BLWA = Black Warrior River AL CARI = Caribou Creek AK
COMO = Como Creek CO
CRAM = Crampton Lake WI
CUPE = Rio Cupeyes PR
FLNT = Flint River GA
GUIL = Rio Yahuecas PR HOPB = Lower Hop Brook MA
KING = Kings Creek KS
LECO = LeConte Creek TN
LEWI = Lewis Run VA
LIRO = Little Rock Lake WI
MART = Martha Creek WA MAYF = Mayfield Creek AL
MCDI = McDiffett Creek KS
MCRA = McRae Creek OR
OKSR = Oksrukuyik Creek AK
POSE = Posey Creek VA
PRIN = Pringle Creek TX
PRLA = Prairie Lake ND
PRPO = Prairie Pothole ND
REDB = Red Butte Creek UT
SUGG = Suggs Lake FL
SYCA = Sycamore Creek AZ
TECR = Teakettle Creek CA
TOMB = Lower Tombigbee River AL
TOOK = Toolik Lake AK
WALK = Walker Branch TN
WLOU = West St Louis Creek CO

In this exercise, we will pull data from NEON Atlantic Neotropical Domain (D04). The aquatic sites in D04 are Rio Cupeyes (CUPE) and Rio Yahuecas (GUIL). Just substitute the 4-letter site code for any other site at the end of the url.

Now let us download our data. We will focus our exercise on data collected from 2021-10-01 through 2024-09-30 (water years 2022, 2023, 2024). If you are not using a NEON token to download your data, neonUtilities will ignore the token input. We set check.size = F so that the script runs well but remember you always want to check your download size first. For this exercise, we will focus on the following data products:

AIS Data Products:

AOS Data Products:

Download AOS Data Products

# download data of interest - AOS - Macroinvertebrate collection
inv <- neonUtilities::loadByProduct(dpID="DP1.20120.001",
                                    site=c("CUPE","GUIL"), 
                                    startdate="2021-10",
                                    enddate="2024-09",
                                    package="basic",
                                    release= "current",
                                    include.provisional = T,
                                    token = Sys.getenv("NEON_TOKEN"),
                                    check.size = F)

Files Associated with Downloads

The data we’ve downloaded comes as an object that is a named list of objects. To work with each of them, select them from the list using the $ operator.

# view all components of the list
names(inv)
##  [1] "categoricalCodes_20120"      "citation_20120_PROVISIONAL" 
##  [3] "citation_20120_RELEASE-2025" "inv_fieldData"              
##  [5] "inv_persample"               "inv_taxonomyProcessed"      
##  [7] "issueLog_20120"              "readme_20120"               
##  [9] "validation_20120"            "variables_20120"

We can see that there are 10 objects in the downloaded macroinvertebrate collection data.

If you’d like you can use the $ operator to assign an object from an item in the list. If you prefer to extract each table from the list and work with it as independent objects, which we will do, you can use the list2env() function.

# unlist the variables and add to the global environment
list2env(inv,envir = .GlobalEnv)
## <environment: R_GlobalEnv>

Explore: Data Citations

Citing sources correctly helps the NEON user community maintain transparency, openness, and trust, while also providing a benefit of being able to track the impact of NEON on scientific research. Thus, each download of NEON data comes with proper citations custom to to the download that align with NEON’s data citation guidelines

# view formatted citations for DP1.20120.001 download
cat(citation_20120_PROVISIONAL)
## @misc{DP1.20120.001/provisional,
##   doi = {},
##   url = {https://data.neonscience.org/data-products/DP1.20120.001},
##   author = {{National Ecological Observatory Network (NEON)}},
##   language = {en},
##   title = {Macroinvertebrate collection (DP1.20120.001)},
##   publisher = {National Ecological Observatory Network (NEON)},
##   year = {2025}
## }
cat(`citation_20120_RELEASE-2025`)
## @misc{https://doi.org/10.48443/rmeq-8897,
##   doi = {10.48443/RMEQ-8897},
##   url = {https://data.neonscience.org/data-products/DP1.20120.001/RELEASE-2025},
##   author = {{National Ecological Observatory Network (NEON)}},
##   keywords = {diversity, taxonomy, community composition, species composition, population, aquatic, benthic, macroinvertebrates, invertebrates, abundance, streams, lakes, rivers, wadeable streams, material samples, archived samples, biodiversity},
##   language = {en},
##   title = {Macroinvertebrate collection (DP1.20120.001)},
##   publisher = {National Ecological Observatory Network (NEON)},
##   year = {2025}
## }

Explore: Metadata

  • categoricalCodes_xxxxx: Some variables in the data tables are published as strings and constrained to a standardized list of values (LOV). This file shows all the LOV options for variables published in this data product.
  • issueLog_xxxxx: Issues that may impact data quality, or changes to a data product that affects all sites, are reported in this file.
  • readme_xxxxx: The readme file provides important information relevant to the data product and the specific instance of downloading the data.
  • validation_xxxxx: If any fields require validation prior to publication, those validation rules are reported in this table
  • variables_xxxxx: This file contains all the variables found in the associated data table(s). This includes full definitions, units, and other important information.
# view the entire dataframe in your R environment
view(variables_20120)

Explore: Dataframes

There will always be one or more dataframes that include the primary data of the data product you downloaded. Multiple dataframes are available when there are related datatables for a single data product.

# view the entire dataframe in your R environment
view(inv_fieldData)

Download AIS Data Products

# download data of interest - AIS - Continuous discharge
csd <- neonUtilities::loadByProduct(dpID="DP4.00130.001",
                                    site=c("CUPE","GUIL"), 
                                    startdate="2021-10",
                                    enddate="2024-09",
                                    package="basic",
                                    release= "current",
                                    include.provisional = T,
                                    token = Sys.getenv("NEON_TOKEN"),
                                    check.size = F)

Let’s see what files are included with an AIS data product download

# view all components of the list
names(csd)
## [1] "categoricalCodes_00130"      "citation_00130_PROVISIONAL" 
## [3] "citation_00130_RELEASE-2025" "csd_continuousDischarge"    
## [5] "issueLog_00130"              "readme_00130"               
## [7] "science_review_flags_00130"  "sensor_positions_00130"     
## [9] "variables_00130"

This AIS data product contains 1 data table available in the basic package:

Additionally, there are a couple of metadata file types included in AIS data product downloads that are not included in AOS data product downloads:

Let’s unpack the AIS data product to the environment:

# unlist the variables and add to the global environment
list2env(csd, .GlobalEnv)
## <environment: R_GlobalEnv>

Wrangling AOS Data

The neonOS R package was developed to aid in wrangling NEON Observational Subsystem (OS) data products. Two functions used in this exercise are:

Removing Duplicates from OS Data

Duplicates can arise in data, but the neonOS::removeDups() function identifies duplicates in a data table based on primary key information reported in the variables_xxxxx files included in each data download.

Let’s check for duplicates in macroinvertebrate collection data

# what are the primary keys in inv_fieldData?
message("Primary keys in inv_fieldData are: ",
        paste(variables_20120$fieldName[
          variables_20120$table=="inv_fieldData"
          &variables_20120$primaryKey=="Y"
        ],
        collapse = ", ")
        )
# identify duplicates in inv_fieldData
inv_fieldData_dups <- neonOS::removeDups(inv_fieldData,
                                         variables_20120)

# what are the primary keys in inv_persample?
message("Primary keys in inv_persample are: ",
        paste(variables_20120$fieldName[
          variables_20120$table=="inv_persample"
          &variables_20120$primaryKey=="Y"
        ],
        collapse = ", ")
        )
# identify duplicates in inv_persample
inv_persample_dups <- neonOS::removeDups(inv_persample,
                                         variables_20120)

# what are the primary keys in inv_taxonomyProcessed?
message("Primary keys in inv_taxonomyProcessed are: ",
        paste(variables_20120$fieldName[
          variables_20120$table=="inv_taxonomyProcessed"
          &variables_20120$primaryKey=="Y"
        ],
        collapse = ", ")
        )
# identify duplicates in inv_taxonomyProcessed
inv_taxonomyProcessed_dups <- neonOS::removeDups(inv_taxonomyProcessed,
                                         variables_20120)

Thankfully, there are no duplicates in any of the AOS tables used in this exercise!

Joining OS Data Tables

Every NEON data product comes with a Quick Start Guide (QSG). The QSGs contain basic information to help users familiarize themselves with the data products, including description of the data contents, data quality information, common calculations or transformations, and, where relevant, algorithm description and/or table joining instructions.

The QSG for Macroinvertebrate collection can be found on the data product landing page: https://data.neonscience.org/data-products/DP1.20120.001

The neonOS::joinTableNEON() function uses the table joining information in the QSG to quickly join two related NEON data tables from the same data product

# join inv_fieldData and inv_taxonomyProcessed
inv_fieldTaxJoined <- neonOS::joinTableNEON(inv_fieldData,inv_taxonomyProcessed)

Now, with field and taxonomy data joined. Individual taxon identifications are easily linked to field data such as collection latitude/longitude, habitat type, sampler type, and substratum class.

Wrangling AIS Data

Data from Different Sensor Locations (HOR)

NEON often collects the same type of data from sensors in different locations. These data are delivered together but you will frequently want to plot the data separately or only include data from one sensor in your analysis. NEON uses the horizontalPosition variable in the data tables to describe which sensor data is collected from. The horizontalPosition is always a three digit number for AIS data.

The Continuous discharge data product is derived from a single horizontalPosition, which corresponds to the sensor co-located with the staff gauge at the site. This is also the location at which all empirical discharge measurements are taken.

Let’s see from which horizontalPosition the Continuous discharge data is published.

# use dplyr from the tidyverse collection to get all unique horizontal positions
csd_hor <- csd_continuousDischarge%>%
  dplyr::distinct(siteID,stationHorizontalID)
print(csd_hor)
##   siteID stationHorizontalID
## 1   CUPE                 110
## 2   GUIL                 110
## 3   GUIL                 132
# GUIL has two horizontal positions because the location of the staff gauge
# changed sometime during this time period. At what date did that occur?
max(csd_continuousDischarge$endDate[
  csd_continuousDischarge$siteID=="GUIL"
  &csd_continuousDischarge$stationHorizontalID=="110"
])
## [1] "2022-12-12 23:58:00 GMT"

At CUPE, the continuous discharge data are published from the 110 position, which is defined as ‘water level sensors mounted to a staff gauge at stream sites’.

At GUIL, until 2022-12-12, the continuous discharge data were published from the 110 position. On 2022-12-12, the position changed to 132, which is defined as ‘stand-alone water level sensors at downstream (S2) locations at stream sites.’

Average continuous discharge to 15-min interval

To make the continuous discharge data easier to work with for this exercise, let’s use different packages from the tidyverse collection to create a 15-min averaged table.

# 15-min average of continuous discharge data
CSD_15min <- csd_continuousDischarge%>%
  dplyr::mutate(roundDate=lubridate::round_date(endDate,"15 min"))%>%
  dplyr::group_by(siteID,roundDate)%>%
  dplyr::summarise(dischargeMean=mean(continuousDischarge,na.rm=T),
                   dischargeCountQF=sum(dischargeFinalQFSciRvw,na.rm = T))

Notice that we included a summation of the science review quality flag (QFSciRvw; binary: 1 = flag, 0 = no flag) fields in the new table.

Plot Data

Now that we have wrangled the data a bit to make it easier to work with, let’s make some initial plots to see the AOS and AIS data separately before we begin to investigate questions that involve integrating the data.

AOS Macroinvertebrate abundance and richness

First, we remove the records collected outside of normal sampling bouts as a grab sample. In cases where the NEON field ecologists see interesting organisms that would not be captured using standard field sampling methods, they can collect a grab sample to be identified by the expert taxonomists.

Next, we calculate macroinvertebrate abundance per square meter and taxon richness per sampling bout. This allows us to compare macroinvertebrate data among different samplerTypes and habitatTypes.

We use the vegan R package to calculate richness, evenness, and both the Shannon and Simpson biodiversity indicies in this exercise. Though we only focus on richness in the plots, users are encouraged to alter the variables to view other indices. For a more detailed dive into NEON biodiversity analyses, see the following NEON tutorial:

Explore and work with NEON biodiversity data from aquatic ecosystems

Sampler types (e.g., surber, hand corer, kicknet) are strongly associated with habitat (i.e., riffle, run, pool) and substrata. At some NEON sites, like D04 CUPE, the same sampler is used in two habitat types (surber in riffle and run) because all habitats at the site have the same cobble substrata. Data users should look at the data to determine how they want to discriminate between sampler or habitat type.

For this exercise, we split abundance and richness by habitatType. To split instead by samplerType, simply do a find+replace of ‘habitatType’ -> ‘samplerType’ throughout the code.

### SHOW BREAKDOWN OF SAMPLER TYPE BY HABITAT TYPE AT EACH SITE ###

sampler_habitat_summ <- inv_fieldTaxJoined%>%
  dplyr::distinct(siteID,samplerType,habitatType)
sampler_habitat_summ  
##   siteID samplerType habitatType
## 1   CUPE      surber      riffle
## 2   CUPE      surber         run
## 3   GUIL        hess        pool
## 4   GUIL      surber      riffle
## 5   GUIL        hess         run
### PLOT ABUNDANCE ###

# using the `tidyverse` collection, we can clean the data in one piped function
inv_abundance_summ <- inv_fieldTaxJoined%>%
  # remove events when no samples were collected (samplingImpractial)
  # remove samples not associated with a bout
  dplyr::filter(is.na(samplingImpractical)
                &!grepl("GRAB|BRYOZOAN",sampleID))%>%
  
  # calculate abundance (individuals per m2^)
  dplyr::mutate(abun_M2=estimatedTotalCount/benthicArea)%>%
  
  # clean `collectDate` column header
  dplyr::rename(collectDate=collectDate.x)%>%
  
  # first, group including `sampleID` and calculate total abundance per sample
  dplyr::group_by(siteID,collectDate,eventID,sampleID,habitatType,boutNumber)%>%
  dplyr::summarize(abun_M2_sum = sum(abun_M2, na.rm = TRUE))%>%
  
  # second, group excluding `sampleID` to summarize by each bout (`eventID`)
  dplyr::group_by(siteID,collectDate,eventID,habitatType,boutNumber)%>%
  
  # summarize to get mean (+/- se) abundance by bout and sampler type
  dplyr::summarise_each(funs(mean,sd,se=sd(.)/sqrt(n())))%>%
  
  # get categorical variable to sort bouts chronologically
  dplyr::mutate(year=substr(eventID, 6,9),
                yearBout=paste(year,"Bout",boutNumber, sep = "."))

# produce stacked plot to show trends within and across sites
inv_abundance_plot <- inv_abundance_summ%>%
  ggplot2::ggplot(aes(fill=habitatType, color=habitatType, y=abun_M2_sum_mean, x=yearBout))+
  ggplot2::geom_point(position=position_dodge(0.5), size=2)+
  ggplot2::geom_errorbar(aes(ymin=abun_M2_sum_mean-abun_M2_sum_se, 
                             ymax=abun_M2_sum_mean+abun_M2_sum_se), 
                         width=0.4, alpha=3.0, linewidth=1,
                         position = position_dodge(0.5))+
  ggplot2::facet_wrap(~siteID,ncol = 1,scales="free_y")+
  ggplot2::theme(axis.text.x = element_text(size = 10, angle = 30, 
                                            hjust = 1, vjust = 1))+
  ggplot2::labs(title = "Mean macroinvertebrates per square meter",
                y = "Abundance Per Square Meter",
                x = "Bout")
inv_abundance_plot

### PLOT RICHNESS ###

inv_richness_clean <- inv_fieldTaxJoined%>%
  # remove events when no samples were collected (samplingImpractial)
  # remove samples not associated with a bout
  dplyr::filter(is.na(samplingImpractical)
                &!grepl("GRAB|BRYOZOAN",sampleID))%>%
  # clean `collectDate` column header
  dplyr::rename(collectDate=collectDate.x)

# extract sample metadata
inv_sample_info <- inv_richness_clean%>%
  dplyr::select(sampleID, domainID, siteID, namedLocation, 
                collectDate, eventID, boutNumber, 
                habitatType, samplerType, benthicArea)%>%
  dplyr::distinct()

# filter out rare taxa: only observed 1 (singleton) or 2 (doubleton) times
inv_rare_taxa <- inv_richness_clean%>%
  dplyr::distinct(sampleID, acceptedTaxonID, scientificName)%>%
  dplyr::group_by(scientificName)%>%
  dplyr::summarize(occurrences = n())%>%
  dplyr::filter(occurrences > 2)

# filter richness table based on taxon list excluding singletons and doubletons
inv_richness_clean <- inv_richness_clean %>%
  dplyr::filter(scientificName%in%inv_rare_taxa$scientificName) 

# create a matrix of taxa by sampleID
inv_richness_clean_wide <- inv_richness_clean %>%
  # subset to unique combinations of `sampleID` and `scientificName`
  dplyr::distinct(sampleID,scientificName,.keep_all = T)%>%
  
  # remove any records with no abundance data
  dplyr::mutate(abun_M2=estimatedTotalCount/benthicArea)%>%
  filter(!is.na(abun_M2))%>%
  
  # pivot to wide format, sum multiple counts per sampleID
  tidyr::pivot_wider(id_cols = sampleID, 
                     names_from = scientificName,
                     values_from = abun_M2,
                     values_fill = list(abun_M2 = 0),
                     values_fn = list(abun_M2 = sum)) %>%
  tibble::column_to_rownames(var = "sampleID") %>%
  
  #round to integer so that vegan functions will run
  round()

# code check - check col and row sums
# mins should all be > 0 for further analysis in vegan
if(colSums(inv_richness_clean_wide) %>% min()==0){
  stop("Column sum is 0: do not proceed with richness analysis!")
}
if(rowSums(inv_richness_clean_wide) %>% min()==0){
  stop("Row sum is 0: do not proceed with richness analysis!")
}

# use the `vegan` package to calculate diversity indices

# calculate richness
inv_richness <- as.data.frame(
  vegan::specnumber(inv_richness_clean_wide)
  )
names(inv_richness) <- "richness"

inv_richness_stats <- vegan::estimateR(inv_richness_clean_wide)

# calculate evenness
inv_evenness <- as.data.frame(
  vegan::diversity(inv_richness_clean_wide)/
    log(vegan::specnumber(inv_richness_clean_wide))
  )
names(inv_evenness) <- "evenness"

# calculate shannon index
inv_shannon <- as.data.frame(
  vegan::diversity(inv_richness_clean_wide, index = "shannon")
  )
names(inv_shannon) <- "shannon"

# calculate simpson index
inv_simpson <- as.data.frame(
  vegan::diversity(inv_richness_clean_wide, index = "simpson")
  )
names(inv_simpson) <- "simpson"

# create a single data frame
inv_diversity_indices <- cbind(inv_richness, inv_evenness, inv_shannon, inv_simpson)

# bring in the metadata table created earlier
inv_diversity_indices <- dplyr::left_join(
  tibble::rownames_to_column(inv_diversity_indices),
  inv_sample_info,
  by = c("rowname" = "sampleID")) %>%
  dplyr::rename(sampleID = rowname)

# create summary table for plotting
inv_diversity_summ <- inv_diversity_indices%>%
  tidyr::pivot_longer(c(richness,evenness,shannon,simpson),
                      names_to = "indexName",
                      values_to = "indexValue")%>%
  group_by(siteID,collectDate,eventID,habitatType,boutNumber,indexName)%>%
  dplyr::summarize(mean = mean(indexValue),
                   n=n(),
                   sd = sd(indexValue),
                   se=sd/sqrt(n))%>%
  dplyr::mutate(year=substr(eventID, 6,9),
                yearBout=paste(year,"Bout",boutNumber, sep = "."))

# produce plot to show trends within and across sites
inv_richness_plot <- inv_diversity_summ%>%
  dplyr::filter(indexName=="richness")%>%
  ggplot2::ggplot(aes(fill=habitatType, color=habitatType, y=mean, x=yearBout))+
  ggplot2::geom_point(position=position_dodge(0.5), size=2)+
  ggplot2::geom_errorbar(aes(ymin=mean-se, ymax=mean+se), 
                         width=0.4, alpha=3.0, linewidth=1,
                         position = position_dodge(0.5))+
  ggplot2::facet_wrap(~siteID,ncol=1)+
  ggplot2::theme(axis.text.x = element_text(size = 10, angle = 30, 
                                            hjust = 1, vjust = 1))+
  labs(title="Mean number of macroinvertebrate taxa per bout",
       y= "Taxon Richness", x = "Bout")

inv_richness_plot

AIS Continuous discharge timseries

Now, let’s visualize the cleaned and gap-filled continuous discharge timeseries for the two NEON D04 sites.

CSD_plot <- CSD_15min%>%
  ggplot2::ggplot(aes(x=roundDate,y=dischargeMean))+
  ggplot2::geom_line()+
  ggplot2::facet_wrap(~siteID,ncol = 1)+
  # ggplot2::scale_y_log10()+ # Include to show discharge axis in log scale
  labs(title="Continuous Discharge for Water Years 2022-2024",
       y= "Discharge (L/s)", x = "Date")

CSD_plot

Visualize AOS and AIS Data Together

Next, we will use the R package plotly to make fun interactive plots allowing us to view AOS and AIS data in the same plotting field. There is a lot of code here to correctly format the plot in a way to provide as much info and be as interactive as possible in a single plotting field.

The plotly package allows us to interact with the plots in the following ways:

INV Abundance and Richness + Discharge Over Time

# choose the site(s) you want to plot
siteToPlot <- c("CUPE","GUIL")

for(s in 1:length(siteToPlot)){
  # begin the plot code
  AOS_AIS_plot <- CSD_15min%>%
    dplyr::filter(siteID==siteToPlot[s])%>%
    plotly::plot_ly()%>%
    # add trace for continuous discharge
    plotly::add_trace(x=~roundDate,y=~dischargeMean,
                      type="scatter",mode="line",
                      line=list(color = 'darkgray'),
                      name="Discharge")%>%
    # add trace for INV abundance
    plotly::add_trace(data=inv_abundance_summ%>%
                        dplyr::filter(siteID==siteToPlot[s]),
                      x=~collectDate,y=~abun_M2_sum_mean,
                      split=~paste0("INV Abundance: ",habitatType),
                      yaxis="y2",type="scatter",mode="line",
                      error_y=~list(array=abun_M2_sum_se,
                                    color='darkorange'),
                      marker=list(color="darkorange"),
                      line=list(color="darkorange"),
                      visible="legendonly")%>%
    # add trace for INV richness
    plotly::add_trace(data=inv_diversity_summ%>%
                        dplyr::filter(siteID==siteToPlot[s]
                                      &indexName=="richness"),
                      x=~collectDate,y=~mean,
                      split=~paste0("INV Richness: ",habitatType),
                      yaxis="y3",type="scatter",mode="line",
                      error_y=~list(array=se,
                                    color='darkgreen'),
                      marker=list(color="darkgreen"),
                      line=list(color="darkgreen"),
                      visible="legendonly")%>%
    # define the layout of the plot
    plotly::layout(
      title = paste0(siteToPlot[s],
                     " Discharge w/ Macroinvertebrate Abundance & Richness"),
      # format x-axis
      xaxis=list(title="dateTime",
                 automargin=TRUE,
                 domain=c(0,0.9)),
      # format first y-axis
      yaxis=list(
        side='left',
        title='Discharge (L/s)',
        showgrid=FALSE,
        zeroline=FALSE,
        automargin=TRUE),
      # format second y-axis
      yaxis2=list(
        side='right',
        overlaying="y",
        title='INV Abundance',
        showgrid=FALSE,
        automargin=TRUE,
        zeroline=FALSE,
        tickfont=list(color = 'darkorange'),
        titlefont=list(color = 'darkorange')),
      # format third y-axis
      yaxis3=list(
        side='right',
        overlaying="y",
        anchor="free",
        title='INV Richness',
        showgrid=FALSE,
        zeroline=FALSE,
        automargin=TRUE,
        tickfont=list(color = 'darkgreen'),
        titlefont=list(color = 'darkgreen'),
        position=0.99),
      # format legend
      legend=list(xanchor = 'center',
                  yanchor = 'top',
                  orientation = 'h',
                  x=0.5,y=-0.2),
      # add button to switch discharge between linear and log
      updatemenus=list(
        list(
          type='buttons',
          buttons=list(
            list(label='linear',
              method='relayout',
              args=list(list(yaxis=list(type='linear')))),
            list(label='log',
              method='relayout',
              args=list(list(yaxis=list(type='log'))))))))
  
  assign(paste0("AOS_AIS_plot_",siteToPlot[s]),AOS_AIS_plot)
}

# show plot at CUPE
AOS_AIS_plot_CUPE
# show plot at GUIL
AOS_AIS_plot_GUIL

Now, we can take what we have learned about NEON AOS and AIS data and look at other case studies using different NEON data products.